Monitoring and Predicting the Microbial Water Quality in Irrigation Ponds

Abstract

Small- to medium-sized farm ponds are a popular source of irrigation water and provide a substantial volume of water for crop growth in the United States. The microbial quality of irrigation waters is assessed by measuring concentrations of the fecal indicator bacteria Escherichia coli (E. coli). Minimal guidance currently exists on the use of surface irrigation waters to minimize consumer health risks. The overall objective of this work was to provide science-based guidance for microbial water quality monitoring of irrigation ponds. Spatial and temporal patterns of E. coli were evaluated in two Maryland irrigation ponds over three years of observations. Patterns of E. coli were stable over the three years and found to be significantly correlated to patterns of water parameters such as temperature, dissolved oxygen, turbidity, and pH. The EPA Environmental Fluid Dynamics Code model was used to evaluate the spatial 3D heterogeneity of E. coli concentrations within the ponds. Significant differences in E. coli concentrations by sampling depth were found. Spatial heterogeneity of E. coli within the pond also resulted in substantial temporal variation at the irrigation pump, which was dependent on the intake location. Diurnal variation of E. coli concentrations was assessed for three farm ponds. E. coli concentrations declined from 9:00 to 15:00 for each pond, but statistically significant declines were only observed in two of the three ponds. Dissolved oxygen, pH, and electrical conductance were found to be the most influential environmental variables affecting E. coli concentrations. To better describe the relationships between E. coli and the environmental variables, four machine learning algorithms were used to estimate E. coli concentrations using water quality parameters as predictors. The random forest algorithm provided the highest predictive accuracy with R2 = 0.750 and R2 = 0.745 for Ponds 1 and 2, respectively, in the multi-year dataset containing 12 predictors. Temperature, electrical conductance, and organic matter content were identified as the most influential predictors. It is anticipated that the recommendations contained in this dissertation will be used to improve microbial monitoring strategies and protect public health.